Abstract
In this study, a hybrid algorithm of adaptive neuro fuzzy inference system (ANFIS), particle swarm optimization (PSO) and principle component analysis (PCA) is utilized to predict the reference evapotranspiration (ET0). The accuracy of the computational model is evaluated using four statistical tests including Pearson correlation coefficient (r), mean square error (MSE), root mean-square error (RMSE), and coefficient of determination (R2). The results show that the ET0 can be estimated with an acceptable accuracy trough combination of PCA and ANFIS. Moreover, the result indicated that the ANFIS model can be simplified via reducing dimensionality of the input data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Pereira, L., et al.: Crop evapotranspiration estimation with FAO56: past and future. Agric. Water Manag. 147, 4–20 (2015)
Allen, R.G., et al.: Crop Evapotranspiration. Guidelines for Computing Crop Water Requirements, vol. 56. FAO Irrigation and Drainage Paper, Roma (1998)
Raziei, T., Pereira, L.S.: Spatial variability analysis of reference evapotranspiration in Iran. Agric. Water Manag. 126, 104–118 (2013)
Mattar, M.A.: Using gene expression programming in monthly reference evapotranspiration modeling: a case study in Egypt. Agric. Water Manag. 198, 28–38 (2018)
Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst. 13(2), 87–129 (2001)
Awange, J.L., et al.: Support Vector Machines (SVM). In: Mathematical Geosciences, pp. 279–320. Springer, Berlin (2018)
Gavili, S., et al.: Evaluation of several soft computing methods in monthly evapotranspiration modelling. Meteorol. Appl. 25(1), 128–138 (2018)
Keskin, M., et al.: Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor. Appl. Climatol. 98(1–2), 79–87 (2009)
Karimaldini, F., et al.: Daily evapotranspiration modeling from limited weather data using neuro-fuzzy computing technique. J. Irrig. Drain. Eng. 138(1), 21–34 (2012)
Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: Advances in Intelligent Systems and Computing. Springer, Berlin (2018)
Torabi, M., et al.: A hybrid machine learning approach for daily prediction of solar radiation. In: Recent Advances in Technology Research and Education (2018)
Najafi, B., et al.: An intelligent artificial neural network-response surface methodology. Energies 11(4), 860 (2018)
Moeini, I., et al.: Modeling the time-dependent characteristics of perovskite solar cells. Sol. Energy 170, 969–973 (2018)
Mosavi, A., et al.: Reviewing the novel machine learning tools for materials design. In: Advances in Intelligent Systems and Computing, pp. 50–58 (2017)
Moeini, I., et al.: Modeling the detection efficiency in photodetectors with temperature-dependent mobility and carrier lifetime. Superlattices Microstruct. (2018)
Dineva, A., et al.: Fuzzy expert system for automatic wavelet shrinkage procedure selection for noise suppression. In: Intelligent Engineering Systems, pp. 163–168 (2014)
Mosavi, A.: The large scale system of multiple criteria decision making. Large Scale Complex Syst. Theory Appl. 9(1), 354–359 (2010)
Mosavi A., et al.: Predicting the future using web knowledge: state of the art survey. In: Advances in Intelligent Systems and Computing, vol 660 (2018)
Jackson, J.E.: A User’s Guide to Principle Components. Wiley, London (1991)
Mosavi, A., Varkonyi-Koczy, A.R.: Integration of machine learning and optimization for robot learning. In: Advances in Intelligent Systems and Computing (2017)
Mosavi, A., et al.: Industrial applications of big data: state of the art survey. Adv. Intell. Syst. Comput. 660, 225–232 (2017)
Karimi, I., Salahshoor, K.: A new fault detection and diagnosis approach for a distillation column based on a combined PCA and ANFIS. In: Control and Decision, pp. 3408–3413 (2012)
Mosavi, A., et al.: Review on the usage of the multiobjective optimization package of modefrontier in the energy sector. In: Advances in Intelligent Systems and Computing, pp. 217–224 (2017)
Imani, M.H., et al.: Strategic behavior of retailers for risk reduction and profit increment via distributed generators and demand response programs. Energies 11(6), 1–24 (2018)
Gocic, M., Trajkovic, S.: Software for estimating reference evapotranspiration using limited weather data. Comput. Electron. Agric. 71(2), 158–162 (2010)
Kumar, M., et al.: Estimating evapotranspiration using artificial neural network. J. Irrig. Drain. Eng. 128(4), 224–233 (2002)
Acknowledgment
Dr. Mosavi carried out this research during the tenure of an ERCIM Alain Bensoussan Fellowship Programme. Furthermore, the support and research infrastructure of Institute of Advanced Studies Koszeg, iASK, is acknowledged.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mosavi, A., Edalatifar, M. (2019). A Hybrid Neuro-Fuzzy Algorithm for Prediction of Reference Evapotranspiration. In: Laukaitis, G. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2018. Lecture Notes in Networks and Systems, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-99834-3_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-99834-3_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99833-6
Online ISBN: 978-3-319-99834-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)